5 citations · 9 across the 5 of their papers we have counts for
6 papers
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari +7
Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of…
Interactive Imitation Learning in State-Space
Snehal Jauhri, Carlos Celemin, Jens Kober
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available…
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems…
Deep Reinforcement Learning with Feedback-based Exploration
Jan Scholten, Daan Wout, Carlos Celemin +1
Deep Reinforcement Learning has enabled the control of increasingly complex and high-dimensional problems. However, the need of vast amounts of data before reasonable performance i…
Learning Gaussian Policies from Corrective Human Feedback
Daan Wout, Jan Scholten, Carlos Celemin +1
Learning from human feedback is a viable alternative to control design that does not require modelling or control expertise. Particularly, learning from corrective advice garners a…
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demandin…